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一种新型自动持续气道正压通气设备评估阻塞性睡眠呼吸暂停患者残余呼吸暂停低通气指数的准确性

Accuracy of a novel auto-CPAP device to evaluate the residual apnea-hypopnea index in patients with obstructive sleep apnea.

作者信息

Nigro Carlos Alberto, González Sergio, Arce Anabella, Aragone María Rosario, Nigro Luciana

机构信息

Sleep Laboratory, Hospital Alemán, Pedro Goyena 620 3 B, CP 1424, Buenos Aires, Argentina,

出版信息

Sleep Breath. 2015 May;19(2):569-78. doi: 10.1007/s11325-014-1048-z. Epub 2014 Aug 13.

Abstract

BACKGROUND

Patients under treatment with continuous positive airway pressure (CPAP) may have residual sleep apnea (RSA).

OBJECTIVE

The main objective of our study was to evaluate a novel auto-CPAP for the diagnosis of RSA.

METHODS

All patients referred to the sleep laboratory to undergo CPAP polysomnography were evaluated. Patients treated with oxygen or noninvasive ventilation and split-night polysomnography (PSG), PSG with artifacts, or total sleep time less than 180 min were excluded. The PSG was manually analyzed before generating the automatic report from auto-CPAP. PSG variables (respiratory disturbance index (RDI), obstructive apnea index, hypopnea index, and central apnea index) were compared with their counterparts from auto-CPAP through Bland-Altman plots and intraclass correlation coefficient. The diagnostic accuracy of autoscoring from auto-CPAP using different cutoff points of RDI (≥5 and 10) was evaluated by the receiver operating characteristics (ROCs) curve.

RESULTS

The study included 114 patients (24 women; mean age and BMI, 59 years old and 33 kg/m(2); RDI and apnea/hypopnea index (AHI)-auto median, 5 and 2, respectively). The average difference between the AHI-auto and the RDI was -3.5 ± 3.9. The intraclass correlation coefficient (ICC) between the total number of central apneas, obstructive, and hypopneas between the PSG and the auto-CPAP were 0.69, 0.16, and 0.15, respectively. An AHI-auto >2 (RDI ≥ 5) or >4 (RDI ≥ 10) had an area under the ROC curve, sensitivity, specificity, positive likelihood ratio, and negative for diagnosis of residual sleep apnea of 0.84/0.89, 84/81%, 82/91%, 4.5/9.5, and 0.22/0.2, respectively.

CONCLUSIONS

The automatic analysis from auto-CPAP (S9 Autoset) showed a good diagnostic accuracy to identify residual sleep apnea. The absolute agreement between PSG and auto-CPAP to classify the respiratory events correctly varied from very low (obstructive apneas, hypopneas) to moderate (central apneas).

摘要

背景

接受持续气道正压通气(CPAP)治疗的患者可能存在残余睡眠呼吸暂停(RSA)。

目的

我们研究的主要目的是评估一种新型自动CPAP用于RSA诊断的效果。

方法

对所有转诊至睡眠实验室接受CPAP多导睡眠监测的患者进行评估。排除接受氧气或无创通气治疗以及进行分夜多导睡眠监测(PSG)、存在伪差的PSG或总睡眠时间少于180分钟的患者。在自动CPAP生成自动报告之前,对PSG进行人工分析。通过Bland-Altman图和组内相关系数,将PSG变量(呼吸紊乱指数(RDI)、阻塞性呼吸暂停指数、低通气指数和中枢性呼吸暂停指数)与其在自动CPAP中的对应变量进行比较。使用不同的RDI截断点(≥5和10)评估自动CPAP自动评分对残余睡眠呼吸暂停的诊断准确性,通过受试者操作特征(ROC)曲线进行分析。

结果

该研究纳入了114例患者(24例女性;平均年龄和体重指数分别为59岁和33kg/m²;自动CPAP的RDI和呼吸暂停/低通气指数(AHI)中位数分别为5和2)。自动CPAP的AHI与RDI之间的平均差值为-3.5±3.9。PSG与自动CPAP之间中枢性呼吸暂停、阻塞性呼吸暂停和低通气总数的组内相关系数(ICC)分别为0.69、0.16和0.15。自动CPAP的AHI>2(RDI≥5)或>4(RDI≥10)时,诊断残余睡眠呼吸暂停的ROC曲线下面积、敏感性、特异性、阳性似然比和阴性似然比分别为0.84/0.89、84/81%、82/91%、4.5/9.5和0.22/0.2。

结论

自动CPAP(S9 Autoset)的自动分析在识别残余睡眠呼吸暂停方面显示出良好的诊断准确性。PSG与自动CPAP在正确分类呼吸事件方面的绝对一致性从非常低(阻塞性呼吸暂停、低通气)到中等(中枢性呼吸暂停)不等。

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